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A novel stable feature selection algorithm for machine learning based intrusion detection system
The advent of new technologies like artificial intelligence, and big data has influenced many cyber attackers to launch their attacks on the network. Hence researchers have already proposed Intrusion Detection Systems by incorporating machine learning as well. Building an effective IDS is still a challenging task because of low accuracy. Managing high dimensional data is another major problem that occurs in IDS. Hence in this paper, an efficient Machine Learning based Intrusion Detection System is developed by means of a novel stable feature selection strategy called IV-RFE. The proposed methodology aims to select only the relevant features that contribute to the attack, which is purely based on relative variance, and weight factor in combination with RFE. This methodology increases the performance in terms of accuracy and maintains a stable set of features. Previous studies only focussed on the feature selection strategy and their performance. The feature stability also has to be considered which is an equally important metric, especially in the field of Intrusion Detection Systems. Hence in the current study, an efficient ML based IDS is proposed which selects only the relevant and stable features. Experimental results also revealed that the proposed IV-RFE outperformed well for three attacks with respect to accuracy and stability metrics also. The results show that stability is also an important indicator in selecting the features in the field of Intrusion Detection Systems. 2025 The Authors. Published by Elsevier B.V. -
A novel SIW based dual-band power divider using double-circular complementary split ring resonators
This article presents a novel design of substrate integrated waveguide (SIW) dual-band power divider loaded with double-circular complementary split-ring resonators (CSRRs). The double-circular CSRRs are etched on the top layer of the proposed structure to obtain the dual-band characteristic. The proposed geometry provides a passband frequency below the cut-off frequency of the SIW due to the electric dipole nature of the CSRRs. By changing the dimensions of the CSRRs, various passband characteristics are studied. To validate the design idea, a compact dual band power divider with equal power division operating at 8.4 and 11.7 GHz is designed, fabricated, and tested. A good steadiness is found between simulated and tested results. The proposed idea provides features of compact size, dual-band operation, and good isolation. The size of the fabricated prototype excluding microstrip transition is 0.473?g 0.284?g, where ?g is the guided wave length at the center frequency of first band. 2019 Wiley Periodicals, Inc. -
A Novel SHiP Vector Machine for Network Intrusion Detection
In this paper, network intrusion detection is proposed using an improved version of the support vector machine model to detect DoS attacks. Here, the SVM model considers the weight parameter along with the kernel to find the best decision boundary that separates the data into DoS and normal. The proposed model provides a novel kernel trick that reduces the overlapping of data. The intrusion detection system aims to construct an ideal system that can detect attacks with very high performance using a ShiP vector machine(Sophisticated High Performance Vector Machine). The framework comprises three major steps: data collection and preprocessing, Recursive Feature Elimination (RFE) based feature selection, and the ShiP Vector Machine classification strategy. The system is evaluated using the DoS dataset from UNSWNB15 and real time PSD-23 sniffer dataset. DoS data is generated by extracting the normal and DoS attacks from the UNSWNB15 dataset. Experimental results show that the proposed ShiP vector machine shows outstanding performance by achieving 96.44 % accuracy on the DoS dataset and 90.12 % accuracy for real time PSD-23 data. 2013 IEEE. -
A novel security framework for healthcare data through IOT sensors
The Internet of Things (IoT) has played a crucial role in the distribution of health records and poses security issues to the patient-specific health information needed for remote hospital attention. The majority of publicly accessible security mechanisms for health information do not concentrate on the flow of information from IoT different sensors installed upon the person's blood through networking devices to primary health care centers. In this paper, we investigated the potential risks of unprotected transmission data, particularly among IoT sensor systems and network gateways. The study encourages the transmission of health insurance data to hospitals remotely. The proposed health care information model would encode immediately so that the sensing element before even being transferred to cryptographic techniques. To use a laboratory configuration with two-stage cryptography at the IoT sensor and two-stage decoding at the physician's surgery receptor, the prototype system was validated. The test results for a complete safety system for IoT - based on the transmission of healthcare data seem good. The study opens up new avenues for information security on IoT devices. 2022 The Authors -
A novel secured ledger platform for real-time transactions
The present disclosure relates to a new centralized ledger technology with a centralized validation process. It offers a single platform for all categories of real-time transactions and validations, unlike existing conventional blockchain technology. It offers three levels of hashing placed at the generator, server, and validator end for data security from data tampering and two levels of encryption for communication lines between generator-server and server-validator for packet security. This system ensures trustworthiness, authenticity, and CIA (confidentiality, integrity, and availability) to its end users while being real-time in execution. The proposed system does not follow a chain-based file architecture. Due to this, no concept of chain break arises, and the problems that arise as a result of chain break in the blockchain are avoided. 2022 Elsevier Inc. All rights reserved. -
A novel scheme for energy enhancement in wireless sensor networks
Wireless sensor networks consists of a large amount of miniaturized battery-powered wireless networked sensors which are intended to function for years without any human intervention. Because of the large number of sensors and the restrictions on the environment of their deployment, replacing the components cannot be thought of. So the only viable way out is to efficiently use the available resources. Energy efficiency is a major matter of concern in such networks even though energy harvesting techniques exists. Recent times have shown a growing interest on understanding and developing new strategies of wireless sensor network routing especially focussing on the optimal use of the limited and constrained resources like energy, memory and processing capabilities. Routing have to be given due importance as it consumes major part of the energy compared to that of sensing and processing. Adopting the natures self organising system intelligence for the emerging technologies is quite interesting and has proved to be efficient. This article sheds some light on the existing bio inspired routing protocols and explains a new procedure with mobile sinks for energy efficient routing in wireless sensor networks. 2015 IEEE. -
A novel route for isomerization of ?-pinene oxide at room temperature under irradiation of light-emitting diodes
Present investigation demonstrates the potential use of HY-zeolite for photochemical applications in the selective isomerization of ?-pinene oxide to carveol. In this study, ultraviolet lamp and LED (390 nm) light sources were employed under atmospheric conditions. The results revealed that light penetration through protonated zeolite cavity promotes the hydrogen radical formation, facilitating the isomerization reaction in the presence of dimethylacetamide solvent to achieve up to 60% and 40% conversion of ?-pinene oxide to selective carveol (71%) under light irradiation. Here, using in situ spectroscopic studies (EPR and fluorescence), to confirm the hydrogen radical generation after light irradiation on the reaction mixture. Besides, the mechanistic pathway is proposed based on the experimental evidence of the formation of radicals, which is validated by the Density Functional Theory (DFT). By comparing electrical energy consumption for the same reaction using different reaction setups, it is understood that the energy requirement is nearly the same in the case of a reaction performed using a thermal reactor. The power consumption in reactions conducted using thermal, UV lamp and LED-based reactors was 1.6 kW/h, 1.5 kW/h, and 0.00144 kW/h, respectively. It is clear that the energy consumption in thermal and UV lamp-based reactors is higher than that of LED-based reactors, which was 1111 and 1041 times more than LED reactors respectively. Notably, the catalyst was found to be recyclable at least five consecutive runs, and the successful protocol was demonstrated up to 50 g scale. 2023 Elsevier Ltd -
A Novel Ridge Estimator for the Liu-Type Logistic Regression Model and Its Application to Demographic Data from Urban Slums in Karnataka
This study introduces new ridge estimators for the Liu-type logistic regression model which helps to improve the model performance if multicollinearity is present in the independent variables. Logistic regression is the regression that helps to model binary outcomes but it provides inaccurate and unstable regression coefficients in the presence of multicollinearity. As a result of this, the variance might increase and the predictive accuracy of the model gets reduced. To overcome this issue, the Liu-type logistic regression is used which uses ridge and Liu parameters to provide stable and accurate regression coefficients. Several ridge estimators are proposed in this study based on the Liu-type logistic model which can handle multicollinearity and give better predictive performance of the model. The proposed estimators have been tested on the demographic dataset from Urban Slums in Karnataka and through the empirical analysis it is observed that one among the new ridge estimators give the lowest Mean Square Error (MSE) when compared to the existing ridge estimators. The results show the usefulness of the new estimators to improve the performance of the model and also contribute to the betterment of the logistic regression techniques. This work highlights the critical need to handle multicollinearity in regression analysis and sets the path for researchers to further improve the estimators in the future. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
A Novel Real-Time Posture Monitoring System Using Signal Processing and Computer Vision Techniques
This paper presents a novel real-time posture monitoring system using signal processing and computer vision techniques to provide accurate feedback on body posture. By measuring key angles between the head-shoulder and shoulder-hip regions, the system identifies deviations from ideal posture. A Butterworth low-pass filter is employed to smooth the posture data, significantly reducing noise and misclassification of sudden movements as poor posture. The proposed systems novelty lies in the integration of signal processing to enhance data interpretation, ensuring that momentary shifts are filtered out, resulting in more reliable classification and feedback. The system was tested in real-world scenarios, demonstrating its ability to offer immediate, high-accuracy posture feedback. Unlike conventional systems that rely solely on raw data, our approach uses smoothed, noise-free data to provide a clearer understanding of posture, making it suitable for deployment in workplaces, home offices, and rehabilitation centers. Future work will focus on multi-joint analysis, duration-based feedback mechanisms for sustained posture deviations, and the impact of camera angle on measurement accuracy. Overall, the system provides a cost-effective and efficient solution for continuous posture monitoring, aiming to improve health and ergonomics across various settings. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
A Novel Preprocessing Technique to Aid the Detection of Infected Areas of CT Images in COVID-19 Patients Artificial Intelligence (AI) for Communication Systems
An innovative preprocessing method for discerning infected areas in CT images of COVID-19 is described in this abstract. The methodology being suggested exploits the capabilities of artificial intelligence (AI) to improve disease detection communication systems. By employing sophisticated AI algorithms to preprocess CT images, the method seeks to increase the precision and effectiveness of COVID-19-associated area detection. The incorporation of artificial intelligence (AI) into communication systems facilitates enhanced image analysis, resulting in improved diagnostic capabilities and treatment strategizing. The study's findings demonstrate the potential of preprocessing techniques powered by artificial intelligence in augmenting communication systems with the aim of enhancing healthcare outcomes. 2024 IEEE. -
A Novel Preprocessing Model for Multi Modal Brain MRI image Classification for Stroke Prognosis
Magnetic Resonance Imaging (MRI) is an imaging technique used for the diagnosis and observing the progression in various neurological disorders. Stroke is one of the prominent neurological disorders that creates significant impacts in the patients. It occurs when the blood supply to part of the brain is interrupted or reduced, preventing brain tissues from getting oxygen and nutrients. Multimodal data from various modalities help clinicians in proper prognosis of stroke. Ischemic Stroke Lesion Segmentation Challenge (ISLES22) provides data of stroke data for various stroke patients, the dataset consists of three modalities of data Fluid Attenuated Inversion Recovery (FLAIR), Apparent Diffusion Coefficient (ADC) and Diffusion-Weighted Imaging (DWI). Multimodal data gives a comprehensive understanding of the brain and the stroke lesions. Complex algorithms and processing steps are required to ensure that the data is prepared for further processing. The objective of this experimental research is to create a novel multimodal preprocessing model that can be used for the preprocessing of the multimodal data from various MRI modalities (FLAIR, DWI and ADC). The proposed model supports the automatic removal of artefacts from the multimodal data, by identifying and applying the best preprocessing techniques for Image Registration (Affine or non-rigid transformations), Normalization (Z Score or min-max normalizations), Denoising Techniques (Gaussian, Median, Non-Local Means, or Anisotropic Diffusion filters) and Bias Field correction. The best technique is identified using the evaluation techniques of Dice Coefficient, Signal-to-Noise Ratio (SNR), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM) and Root Mean Squared Error (RMSE). Preprocessing is critical process to improve the outcome of the subsequent analysis including segmentation. Here, we propose an Enhanced Image Registration and Artefact Correction (EIRAC) model with Best Image Registration Technique (BIRT) and Multiple Orientation Normalization Denoising and Bias field correction Parallelly (MONDBP) algorithms for the preprocessing of multimodal MRI images to provides better results for the segmentation of stroke lesions through Machine Learning models. 2025, Binghamton University Libraries. All rights reserved. -
A Novel Paradigm for IoT Security: ResNet-GRU Model Revolutionizes Botnet Attack Detection
The rapid proliferation of the Internet of Things (IoT) has engendered substantial security apprehensions, chiefly due to the emergence of botnet attacks. This research study delves into the realm of Intrusion Detection Systems (IDS) by leveraging the IoT23 dataset, with a specific emphasis on the intricate domain of IoT at the network's edge. The evolution of edge computing underscores the exigency for tailored security solutions. An array of statistical methodologies, encompassing ANOVA, Kruskal-Wallis, and Friedman tests, is systematically employed to illuminate the evolving trends across multiple facets of the study. Given the intricacies entailed in feature selection within edge environments, Chi-square analyses, Recursive Feature Elimination (RFE), and Lasso-based techniques are strategically harnessed to unearth meaningful feature subsets. A meticulous evaluation encompassing 19 classifiers, meticulously selected from both machine learning (ML) and deep learning (DL) paradigms, is rigorously conducted. Initial findings underscore the potential of the Gated Recurrent Unit (GRU) model, especially when coupled with intrinsic lasso-based feature selection. This promising outcome catalyzes the formulation of an ensemble approach that harnesses multiple LassoCV models, aimed at amplifying feature selection proficiency. Furthermore, an optimized ResNet-GRU model emerges from the fusion of the GRU and ResNet architectures, with the objective of augmenting classification performance. In response to mounting concerns regarding data privacy at the edge, a resilient federated learning ecosystem is meticulously crafted. The seamless integration of the optimized ResNet-GRU model into this framework facilitates the employment of FedAvg, a widely acclaimed federated learning methodology, to adeptly navigate the intricacies associated with data sharing challenges. A comprehensive performance evaluation is undertaken, wherein the ResNet-GRU model is benchmarked against FedAvg and a diverse array of other federated learning algorithms, including FedProx and Fed+. This extensive comparative analysis encompasses a spectrum of performance metrics and processing time benchmarks, shedding comprehensive light on the capabilities of the model. (2023), (Science and Information Organization). All Rights Reserved. -
A novel optimised method for speckle reduction in medical ultrasound images
The advancement of medical imaging techniques evolving from X-ray to PET images and the medical image analysis helped medical experts to detect, diagnose and offer treatments for complex disorders and deadly diseases in the human body. Among the various modalities used, Ultrasound imaging is the most widely accepted modality because of its affordability, non-invasive nature and various other features. But the presence of speckle noise in ultrasound image lowers the image quality and reduces diagnostic value. This article states an improved hybrid speckle noise reduction method, a combined application of Kuan and non-local means filters. In this method, Kuan filter is used to sharpen the edges and thereafter the speckle noise elimination is done by using the non-local means. In addition, the performance of the proposed hybrid filter and its design parameters are optimised by using a meta-heuristic called grey wolf optimiser. The performance of hybrid method is evaluated by analysing a chosen set of well-known post filtering methods used for speckle reduction with given ultrasound B-mode images. The comparison of test results using remarkable performance metrics and computation time demonstrate that the hybrid method can be used as the efficient speckle reduction method for image analysis. Copyright 2022 Inderscience Enterprises Ltd. -
A Novel Network-Based Digital Payment Fraud Detection using OP-ELM Network
Internet and Industry 4.0 have helped banks and other financial organizations enhance procedures and decrease fraud. Digital payment techniques have helped internet buying skyrocket. Industry 4.0 promotes process optimization, ecosystem collaboration, and growth by integrating digital systems with physical and IoT devices. Unfortunately, digital payment cybercrime has grown rapidly, causing large annual financial losses. Because of this, fraud detection systems must be constantly improved. The suggested TLELM approach includes preprocessing, feature selection, and model training. Preprocessing involves standardizing data, eliminating outliers, and handling null or missing values. The CSO technique selects relevant features by optimizing selection. A new approach combining TL and ELM improves DPFD procedures. The new metaheuristic TL excels at combinatorial optimization. TLELM efficacy was examined using multiple datasets. The recommended method was compared to top-tier algorithms for binary and multiclass data categorization. Experimental data shows that TLELM outperforms other models with 99.37% accuracy. This study found that TLELM can detect online payment fraud. The method optimizes fraud detection and classification accuracy using TL and ELM. Add more real-world datasets to strengthen robustness and make additional improvements to handle future fraud methods. 2025 IEEE. -
A Novel Nature-Inspired Coconut Tree Optimization Technique forEngineering Applications
Coconut tree optimization technique is a novel optimization algorithm that is motivated by the physical structure of the coconut tree. The search for optima in a feasible region is chosen between a random root and any point in a leaf. Coconut tree optimization is a pseudo meta-heuristic algorithm wherein search of solution is carried out using random as well as gradient movement with a memory stack that contains local optima. Nonlinear optimization problems consisting of equality and inequality constraints were solved using the proposed algorithm. The algorithm is validated for linear and nonlinear optimization problems. The comparative study and analysis were detailed for existing algorithms used in domain-specific physical problems. The algorithm is compared with the genetic algorithm and particle swarm optimization by considering standard test functions. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
A novel multigrade classification in FL using brain MRI images based on FHAT_EfficientNet
This paper establishes the fractional harmony artificial tree (FHAT)_EfficientNet for multi-grade classification in federated learning (FL). Here, the established FHAT is attained by the integration of the fractional calculus (FC) and harmony search-based feedback artificial tree (HSFAT) algorithm, and the HSFAT is developed by the combination of harmony search (HS) and feedback artificial tree (FAT). Initially, the input MRI image is taken from a particular dataset and subjected to pre-processing. Thereafter, tumour segmentation is accomplished based on fuzzy local information c-means (FLICM). Later, image augmentation and feature extraction are performed. Finally, the multi-grade classification is carried out using EfficientNet fine-tuned based on the proposed FHAT. Moreover, the established FHAT_EfficientNet attained better accuracy, specificity, sensitivity, mean squared error (MSE), root mean square error (RMSE), and loss function of 0.917, 0.936, 0.966, 0.058, 0.241, and 0.083. Copyright 2025 Inderscience Enterprises Ltd. -
A novel multi functional multi parameter concealed cluster based data aggregation scheme for wireless sensor networks (NMFMP-CDA)
Data aggregation is a promising solution for minimizing the communication overhead by merging redundant data thereby prolonging the lifetime of energy starving Wireless Sensor Network (WSN). Deployment of heterogeneous sensors for measuring different kinds of physical parameter requires the aggregator to combine diverse data in a smooth and secure manner. Supporting multi functional data aggregation can reduce the transmission cost wherein the base station can compute multiple statistical operations in one query. In this paper, we propose a novel secure energy efficient scheme for aggregating data of diverse parameters by representing sensed data as number of occurrences of different range value using binary encoded form thereby enabling the base station to compute multiple statistical functions over the obtained aggregate of each single parameter in one query. This also facilitates aggregation at every hop with less communication overhead and allows the network size to grow dynamically which in turn meets the need of large scale WSN. To support the recovery of parameter wise elaborated view from the multi parameter aggregate a novelty is employed in additive aggregation. End to end confidentiality of the data is secured by adopting elliptic curve based homomorphic encryption scheme. In addition, signature is attached with the cipher text to preserve the data integrity and authenticity of the node both at the base station and the aggregator which filters out false data at the earliest there by saving bandwidth. The efficiency of the proposed scheme is analyzed in terms of computation and communication overhead with respect to various schemes for various network sizes. This scheme is also validated against various attacks and proved to be efficient for aggregating more number of parameters. To the best of our understanding, our proposed scheme is the first to meet all of the above stated quality measures with a good performance. 2020, Springer Science+Business Media, LLC, part of Springer Nature. -
A novel moems sensor design simulation and analysis with MEEP /
International Journal Of Engineering Technology Science And Research, Vol.2, Issue 8, pp.319-325, ISSN No: 2394-3386. -
A novel model for speech to text conversion /
International Refered Journal of Engineering And Science, Vol-3 (1), pp. 39-41,ISSN-2319-183X. -
A novel mobile sink placement in wireless sensor network using deep maxout network based energy prediction with adjacent cell score
The majority of Wireless Sensor Networks (WSNs) are made up of energy- and cost-efficient detecting nodes. Traditional wireless sensor networks encounter serious problems, including latency, network failure, and congestion, since they rely on individual base stations (BSs) to gather data from the whole network. Sensor nodes adjacent to the base station will use more energy because of excessive energy consumption and energy-hole constraints, affecting the network's life. Understanding the best place for mobile sink nodes can help alleviate this issue by lowering energy usage and extending the network's lifespan. In this paper, utilizing a deep learning-based energy prediction and neighbour cell score model, we build and construct an efficient method to locate mobile receivers using distance, expected energy, and fairness variables. Furthermore, a Deep Maximum Output Network (DMN) calculates the desired power. However, the minimum length, maximum residual energy, complete normalized right, maximum network lifespan, and maximum normalized throughput for our suggested neighbor-based cell scoring with Deep Maxout Network are 137.364, 30.903, 64.426, and 60.613, respectively. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.